Air Bearing Lift Pad (ABLP)

Typical air bearings float on air films of only a few thousandths of an inch and so will only operate above very smooth, even surfaces. For the mechanical simulation of space, the small drag of the bladder type air pads is much more than can be coped with, and the practicality of large floor areas being machined for precision air bearings is nonexistent. To enable operation above surfaces that undulate slightly or feature cracks and discontinuities, an ABLP has been developed. It consists of a rigid pad beneath which an inflatable bladder is mounted. The bladder is inflated with air which then escapes through passages into a cavity in the center of the bladder to produce the lifting energy. As the air escapes about the perimeter of the bladder, a certain degree of balance and equilibrium is imparted to the pad as it is able to move a limited weight across slightly uneven surfaces

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software